Essay We Must Have a Right to Privacy - 3747 Words

The Information Age has emerged with speed, excitement, and great promise. The electronic eyes and ears of technology follow us everywhere. There are those enamored with the rush of technology, who b elieve that the best of worlds is one in which everyone can peer into everyone elses lives. In fact, we now live in a world consumed with the ecstacy of communication (Karaim 76). Americans line up to reveal their darkest secrets of their m ost intimate moments, or just hang out their dirty laundry on the numerous television talk shows. The more exposure, the better. So it may be absurd that we should worry that our privacy is being endangered, our personal life and even our se crets made public. The loss of privacy is on the†¦show more content†¦One of the most popular devices today, which is used on toll roads, office buildings, banks, and stores to deter crime is the surveillance camera. But how can a law-abiding citizen protect his privacy when he is constantly be ing filmed? The cellular phone, a best seller in the 1990s, provid es convenience of calling while on the go. Are the calls a person makes on these phones confidential? No, a call can be intercepted and people who have police scanners can pick up access numbers. Perhaps as popular as the convenience of using the cellu lar phone, is the ease of paying by credit card. But even the cards can be monitored electronically, making everything that a person purchases known to outsiders (Quittner 32-33). Then, of course, there is the Information Superhighway, whose users numbe red 30 million in 1996. As citizens perform more social and commercial transactions in cyberspace, it becomes easier to track down their spending habits, interests, life styles, and beliefs. A computer expert can take any persons Social Security number and find personal details abou this or her life and the history on the Internet (Everett-Green 158). What is troubling about the issue of privacy, assaults on that privacy by the Information Superhighway, surveillance cameras, electronic tolls, and numerous other high-tech devices, is that there is little, if any, debate about whether such practic es are good forShow MoreRelatedA World Without Secrets By Peter Singer1276 Words   |  6 PagesStruggle Towards Privacy In a Democracy As a growing topic of discussion, privacy in our society has stirred quite some concern. With the increase of technology and social networking our standards for privacy have been altered and the boundary between privacy and government has been blurred. In the article, Visible Man: Ethics in a World Without Secrets, Peter Singer addresses the different aspects of privacy that are being affected through the use of technology. The role of privacy in a democraticRead MoreInternet Privacy1375 Words   |  6 PagesA Right to Privacy? What a Joke! It has become a sad and upsetting fact that in todays society the truth is that the right to ones privacy in the I.T (information technological) world has become, simply a joke. In an electronic media article No place to hide, written by James Norman, two interesting and debatable questions were raised: ‘Are we witnessing the erosion of the demarcation of public and private spaces brought on by the networked economy and new technology? Also, ‘What rolesRead MoreCelebrities and Privacy1609 Words   |  7 Pagescelebrities and other individuals who are famous. For our presentation we will be concentrating on celebrities and whether they should expect their privacy to be respected by the media. Media comes in various forms, with the more common ones being newspapers, tabloids, radio, paparazzi, internet, social media and many more. A conflict of rights? Article 8 of the European Convention on Human Rights stated that every person has the â€Å"right to respect for his private and family life. 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People think that they should be completely secluded from others seeing what they’re doing, but in all reality, there’s no stopping people from seeing what you are doing. With more people using the flaws within our media and lives, we as a society must come toRead MoreSecurity For Liberty : Freedom Of Life, Liberty And Pursuit Of Happiness1101 Words   |  5 Pagesof common good too, right? But there is a borderline between what an individual should and shouldn’t give up. For example, privacy rights. In most cases, people would claim that they have nothing to hide, but the definition of privacy is not covering the atrocious. Privacy is a fundamental value of human right; it is our defense and space permitted to us of being ourselves. 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Corruption in Kenya - 4555 Words

Corruption in Kenya Introduction Corruption is a global phenomenon and is not bound to be found only in the developing countries but also in the developed countries of the world. Corruption crosses boundaries or age and is mentioned in the religious books of old such as the Bible (Deuteronomy Chapter 16, verses 19), and in Chinese dynasty of Qin Dynasty (221-207).[1] Examples of corruption in developed countries include in Russia where the government of Putin went through all methods known to them to corruptly take away the petroleum mining from the owner,[2]who also is accused of having acquired the wealth corruptly under the leadership of Boris Yeltin’s rule which cost Russia its valued national resources and gave it to the oligarchy†¦show more content†¦In the Kenyan scope, corruption is defined to include â€Å"benefit which means any gift, loan, fee, reward, appointment, service, favour, forbearance, promise or other consideration or advantage; corruption referring to an offence under any of th e provisions of sections 39 to 44, 46 and 47; bribery; fraud; embezzlement or misappropriation of public funds; abuse of office; breach of trust; or an offence involving dishonesty - in connection with any tax, rate or impost levied under any Act or under any written law relating to the elections of persons to public office.[13]Together with these are the Economic crimes referring to offences under section 45; or offences involving dishonesty under any written law providing for the maintenance or protection of the public revenue.[14] This wide definition of corruption does not cover any corrupt activities committed in a private entity or fund. This does not however imply that corruption only occurs in the public sector. Instead the network of corruption extends into executive, judiciary, legislative, civil-service, private sector down to the village tycoon and villager who gives gifts toShow MoreRelatedEffect of Corruption on Kenyas Economoc Growth6642 Words   |  27 PagesOF ECONOMICS THE RELATIONSHIP BETWEEN CORRUPTION AND ECONOMIC GROWTH IN KENYA MULEMBO ENOKA X75/3844/2008 GERALD NGILAI MUEMA X74/3741/2008 GITHINJI JOSEPH MULWA X74/3726/2008 WANGARI ELIJAH GACHOHI X75/3777/2008 KIRU JOSEPH KAMAU X74/ 3599 /2008 TABLE OF CONTENTS INTRODUCTION 1 1.1 Background to the study 1 1.1.1 Ministry of Finance Kenya†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦... 2 1.2 Research ProblemRead MoreThe Link Between Corruption and Poverty : Lessons from Kenya Case Studies1419 Words   |  6 PagesThe Link Between Corruption and Poverty : Lessons from Kenya Case Studies INTRODUCTION One thing can be said-the mere fact that corruption has become an item of national preoccupation is paradoxically the first real achievement by Kenyans over corruption Since the end of the last decade the emphasis has moved from building public awareness on corruption issues to understanding the nature of corruption and its effects on the economy, society and politics; understanding the nature of the beast asRead MoreThe Link Between Corruption and Poverty : Lessons from Kenya Case Studies1403 Words   |  6 PagesThe Link Between Corruption and Poverty : Lessons from Kenya Case Studies INTRODUCTION One thing can be said-the mere fact that corruption has become an item of national preoccupation is paradoxically the first real achievement by Kenyans over corruption Since the end of the last decade the emphasis has moved from building public awareness on corruption issues to understanding the nature of corruption and its effects on the economy, society and politics; understanding the nature of the beastRead MoreCorruption Is A Matter Of Great Concern For The Nation1742 Words   |  7 PagesBritish Government in 1963, Kenya has been plagued with corruption. 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Furthermore, the authorRead MoreThe Long Term Effects Of Colonization1603 Words   |  7 Pagescountries around the world that have trouble with corruption; specifically; Kenya. Corruption has plagued Kenya since colonization when the British Empire took over in 1895, and has not lessened over time. The long term effects of colonization by the British is what caused Kenya to be as corrupt as it is today. Before such dishonesties ensued, there were several historical milestones that significantly impacted Kenya. One of them was the day Kenya gained their independence in 1963. There wasRead MoreNigeria And Keny Corruption Essay1546 Words   |  7 PagesThroughout the last 10 years, Nigeria and Kenya have been partly free. Corruption is the greatest indicator among both for the lack of democracy. In Nigeria, corruption stems from the problem with oil, it leads to political violence, repression and unchecked government power. In Kenya, corruption arises from economic interests, causing political instability and hindering development. In addition to that, both experience electoral corruption. Conversely, civil societies active participation in theRead MorePolitical Corruption Essay1401 Words   |  6 PagesPolitical corruption has existed throughout the ages. It believed to be most prominent in positions of power, because of the role money plays in getting people power. However , over the centuries, corruption has changed so much so as to not match a particular definition of corruption, perpetually growing deceptively harder to find (Ebbe). The broadest, most suitable definition which exists today simply states that corruption is any illegal act performed by a politician to produce results whichRead MoreRole Of Government In Government1155 Words   |  5 Pagesrule. 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Simple Linear Regression Free Essays

string(661) " 200 300 400 500 600 700 800 900 1000 Appraised Value \(in Thousands of Dollars\) Review: Inference for Regression We can describe the relationship between x and y using a simple linear regression model of the form  µy = \? 0 \+ \? 1 x 1000 900 Sale Price \(in Thousands of Dollars\) 800 700 600 500 400 300 200 100 0 0 100 200 300 400 500 600 700 800 900 1000 Appraised Value \(in Thousands of Dollars\) response variable y : sale price explanatory variable x: appraised value relationship between x and y : linear strong positive We can estimate the simple linear regression model using Least Squares \(LS\) yielding the following LS regression line: y = 20\." Stat 326 – Introduction to Business Statistics II Review – Stat 226 Spring 2013 Stat 326 (Spring 2013) Introduction to Business Statistics II 1 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 2 / 47 Review: Inference for Regression Example: Real Estate, Tampa Palms, Florida Goal: Predict sale price of residential property based on the appraised value of the property Data: sale price and total appraised value of 92 residential properties in Tampa Palms, Florida 1000 900 Sale Price (in Thousands of Dollars) 800 700 600 500 400 300 200 100 0 0 100 200 300 400 500 600 700 800 900 1000 Appraised Value (in Thousands of Dollars) Review: Inference for Regression We can describe the relationship between x and y using a simple linear regression model of the form  µy = ? 0 + ? 1 x 1000 900 Sale Price (in Thousands of Dollars) 800 700 600 500 400 300 200 100 0 0 100 200 300 400 500 600 700 800 900 1000 Appraised Value (in Thousands of Dollars) response variable y : sale price explanatory variable x: appraised value relationship between x and y : linear strong positive We can estimate the simple linear regression model using Least Squares (LS) yielding the following LS regression line: y = 20. 94 + 1. 069x Stat 326 (Spring 2013) Introduction to Business Statistics II / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 4 / 47 Review: Inference for Regression Interpretation of estimated intercept b0 : corresponds to the predicted value of y , i. We will write a custom essay sample on Simple Linear Regression or any similar topic only for you Order Now e. y , when x = 0 Review: Inference for Regression Interpretation of estimated slope b1 : corresponds to the change in y for a unit increase in x: when x increases by 1 unit y will increase by the value of b1 interpretation of b0 is not always meaningful (when x cannot take values close to or equal to zero) here b0 = 20. 94: when a property is appraised at zero value the predicted sales price is $20,940 — meaningful?! Stat 326 (Spring 2013) Introduction to Business Statistics II 5 / 47 b1 0: y decreases as x increases (negative association) b1 0: y increases as x increases (positive association) here b1 = 1. 069: when the appraised value of a property increases by 1 unit, i. e. by $1,000, the predicted sale price will increase by $1,069. Stat 326 (Spring 2013) Introduction to Business Statistics II 6 / 47 Review: Inference for Regression Measuring strength and adequacy of a linear relationship correlation coe? cient r : measure of strength of linear relationship ? 1 ? r ? 1 here: r = 0. 9723 Review: Inference for Regression Population regression line Recall from Stat 226 Population regression line The regression model that we assume to hold true for the entire population is the so-called population regression line where  µy = ? 0 + ? 1 x, coe? cient of determination r 2 : amount of variation in y explained by the ? tted linear model 0 ? r2 ? 1 here: r 2 = (0. 9723)2 = 0. 9453 ? 94. 53% of the variation in the sale price can be explained through the linear relationship between the appraised value (x) and the sale price (y ) Stat 326 (Spring 2013) Introduction to Business Statistics II 7 / 47  µy — average (mean) value of y in population for ? xed value of x ? — population intercept ? 1 — population slope The population regression line could only be obtained if we had information on all individuals in the population. Stat 326 (Spring 2013) Introduction to Business Statistics II 8 / 47 Review: Inference for Regression Based on the population regression line we can fully describe re lationship between x and y up to a random error term ? y = ? 0 + ? 1 x + ? , where ? ? N (0, ? ) Review: Inference for Regression In summary, these are important notations used for SLR: Description x y Parameters ? 0 ? 1  µy ? Stat 326 (Spring 2013) Introduction to Business Statistics II 9 / 47 Stat 326 (Spring 2013) Description Estimates b0 b1 y e Description Introduction to Business Statistics II 10 / 47 Review: Inference for Regression Review: Inference for Regression Validity of predictions Assuming we have a â€Å"good† model, predictions are only valid within the range of x-values used to ? t the LS regression model! Predicting outside the range of x is called extrapolation and should be avoided at all costs as predictions can become unreliable. Why ? t a LS regression model? A â€Å"good† model allows us to make predictions about the behavior of the response variable y for di? rent values of x estimate average sale price ( µy ) for a property appraised at $223,000: x = 223 : y = 20. 94 + 1. 069 ? 223 = 259. 327 ? the average sale price for a property appraised at $223,000 is estimated to be about $259,327 What is a â€Å"good† model? — answer to this question is not straight forward. We can visually check the validity of the ? tted linear model (through residu al plots) as well as make use of numerical values such as r 2 . more on assessing the validity of regression model will follow. 11 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 12 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II Review: Inference for Regression What to look for: Review: Inference for Regression Regression Assumptions residual plot: Assumptions SRS (independence of y -values) linear relationship between x and  µy for each value of x, population of y -values is normally distributed (? ? ? N) r2 : for each value of x, standard deviation of y -values (and of ? ) is ? In order to do inference (con? dence intervals and hypotheses tests), we need the following 4 assumptions to hold: Stat 326 (Spring 2013) Introduction to Business Statistics II 13 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 14 / 47 Review: Inference for Regression †SRS Assumption† is hardest to check The †Linearity Assumption† and †Constant SD Assumption† are typically checked visually through a residual plot. Recall: residual = y ? y = y ? (b0 + b1 x) The †Normality Assumption† is checked by assessing whether residuals are approximately normally distributed (use normal quantile plot) plot x versus residuals any pattern indicates violation Review: Inference for Regression Stat 326 (Spring 2013) Introduction to Business Statistics II 15 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 16 / 47 Review: Inference for Regression Returning to the Tampa Palms, Florida example: 100 50 Residual 0 -50 -100 -150 0 100 200 300 400 500 600 700 800 900 1000 Review: Inference for Regression Going one step further, excluding the outlier yields 0. 2 0. 1 0. 0 -0. 1 -0. 2 -0. 3 4 4. 5 5 5. 5 log Appraised 6 6. 5 7 Residual Appraised Value (in Thousands of Dollars) Note: non-constant variance can often be stabilized by transforming x, or 0. 5 y , or both: Residual 0. 0 -0. 5 -1. 0 -1. 5 4 4. 5 5 5. 5 log Appraised 6 6. 5 7 outliers/in? uential points in general should only be excluded from an analysis if they can be explained and their exclusion can be justi? ed, e. g. ypo or invalid measurements, etc. excluding outliers always means a loss of information handle outliers with caution may want to compare analyses with and without outliers Stat 326 (Spring 2013) Introduction to Business Statistics II 17 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 18 / 47 Review: Inference for Regression normal quantil e plots Tampa Palms example Residuals Sale Price (in Thousands of Dollars) 100 .01 . 05 . 10 . 25 . 50 . 75 . 90 . 95 . 99 Review: Inference for Regression Residuals log Sale 50 Regression Inference Con? dence intervals and hypotheses tests -3 -2 -1 0 1 2 3 Normal Quantile Plot -50 -100 Need to assess whether linear relationship between x and y holds true for entire population. .01 . 05 . 10 . 25 . 50 . 75 . 90 . 95 . 99 Residuals log Sale without outlier 0. 2 0. 1 0 -0. 1 -0. 2 -0. 3 -3 -2 -1 0 1 2 3 This can be accomplished through testing H0 : ? 1 = 0 vs. H0 : ? 1 = 0 based on the estimates slope b1 . For simplicity we will work with the untransformed Tampa Palms data. Normal Quantile Plot Stat 326 (Spring 2013) Introduction to Business Statistics II 19 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 20 / 47 Review: Inference for Regression Review: Inference for Regression Example: Find 95% CI for ? 1 for the Tampa Palms data set Con? dence intervals We can construct con? dence intervals (CIs) for ? 1 and ? 0 . General form of a con? dence interval estimate  ± t ? SEestimate , where t ? is the critical value corresponding to the chosen level of con? dence C t ? is based on the t-distribution with n ? 2 degrees of freedom (df) Interpretation: Stat 326 (Spring 2013) Introduction to Business Statistics II 21 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 22 / 47 Review: Inference for Regression Review: Inference for Regression Testing for a linear relationship between x and y If we wish to test whether there exists a signi? cant linear relationship between x and y , we need to test H0 : ? 1 = 0 Why? If we fail to reject the null hypothesis (i. e. stick with H0 = ? 1 = 0), the LS regression model reduces to  µy = ? 1 =0 versus Ha : ? 1 = 0 ?0 + ? 1 x ? 0 + 0  · x ? 0 (constant) Introduction to Business Statistics II 24 / 47 = = implying that  µy (and hence y ) is not linearly dependent on x. Stat 326 (Spring 2013) Introduction to Business Statistics II 23 / 47 Stat 326 (Spring 2013) Review: Inference for Regression Review: Inference for Regression Example (Tampa Palms data set): Test at the ? = 0. 05 level of signi? cance for a linear relationship between the appraised value of a property and the sale price Stat 326 (Spring 2013) Introduction to Business Statistics II 25 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 26 / 47 Inference about Prediction Why ? t a LS regression model? The purpose of a LS regression model is to 1 Inference about Prediction 2 estimate  µy – average/mean value of y for a given value of x, say x ? e. g. estimate average sale price  µy for all residential property in Tampa Palms appraised at x ? $223,000 predict y – an individual/single future value of the response variable y for a given value of x, say x ? e. g. predict a future sale price of an individual residential property appraised at x ? =$223,000 Keep in mind that we consider predictions for only one value of x at a time. Note, these two tasks are VERY di? erent. Carefully think about the di? erence! Stat 326 (Spring 2013) Introduction to Business Statistics II 27 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 28 / 47 Inference about Prediction To estimate  µy and to predict a single future y value for a given level of x = x ? we can use the LS regression line y = b0 + b1 x Simply substitute the desired value of x, say x ? , for x: y = b0 + b1 x ? Inference about Prediction In addition we need to know how much variability is associated with the point estimator. Taking the variability into account provides information about how good and reliable the point estimator really is. That is, which range potentially captures the true (but unknown) parameter value? Recall from 226 ? construction of con? dence intervals Stat 326 (Spring 2013) Introduction to Business Statistics II 29 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 0 / 47 Inference about Prediction Much more variability is associated with estimating a single observation than estimating an average — individual observations always vary more than averages!! Inference about Prediction Therefore we distinguish a con? dence interval for the average/mean response  µy and a prediction interval for a single future observation y Both intervals use a t ? critical value from a t-distribution with df = n ? 2. the standard error will be di? erent for each interval: While the point estimator for the average  µy and the future individual value y are the same (namely y = b0 + b1 x ? , the of the two con? dence intervals ! Stat 326 (Spring 2013) Introduction to Business Statistics II 31 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 32 / 47 Inference about Prediction Con? dence interval for the average/mean response  µy Width of the con? dence interval is determined using the standard error SE µ (from estimating the mean response) SE µ can be obtained in JMP Keep in mind that every con? dence interval is always constructed for one speci? c given v alue x ? A level C con? dence interval for the average/mean response  µy , when x takes the value x? is given by y  ± t ? SE µ , where SE µ is the standard error for estimating a mean response. Stat 326 (Spring 2013) Introduction to Business Statistics II 33 / 47 Inference about Prediction Prediction interval for a single (future) value y Again, Width of the con? dence interval is determined using the standard error SE µ (from estimating the mean response) SEy can be obtained in JMP Keep in mind that every prediction interval is always constructed for one speci? c given value x ? A level C prediction interval for a single observation y , when x takes the value x ? is given by y  ± t ? SEy , where SEy is the standard error for estimating a single response. Stat 326 (Spring 2013) Introduction to Business Statistics II 34 / 47 Inference about Prediction The larger picture: Inference about Prediction The larger picture cont’d. Stat 326 (Spring 2013) Introduction to Business Statistics II 35 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 36 / 47 Inference about Prediction Example: An appliance store runs a 5-month experiment to determine the e? ect of advertising on sales revenue. There are only 5 observations. The scatterplot of the advertising expenditures versus the sales revenues is shown below: Bivariate Fit of Sales Revenues (in Dollars) By Advertising expenditure Inference about Prediction Example cont’d: JMP can draw the con? dence intervals for the mean responses as well as for the predicted values for future observations (prediction intervals). These are called con? dence bands: Bivariate Fit of Sales Revenues (in Dollars) By Advertising expenditure 5000 5000 Sales Revenues (in Dollars) 4000 3000 2000 1000 Sales Revenues (in Dollars) 4000 3000 2000 1000 0 0 0 100 200 300 400 500 600 Advertising expenditure (in Dollars) 0 100 200 300 400 500 600 Advertising expenditure (in Dollars) Linear Fit Linear Fit Sales Revenues (in Dollars) = -100 + 7 Advertising expenditure (in Dollars) Stat 326 (Spring 2013) Introduction to Business Statistics II 37 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 38 / 47 Inference about Prediction Inference about Prediction Estimation and prediction (for the appliance store data) Estimation and prediction – Using JMP For each observation in a data set we can get from JMP: y , SEy , and also SE µ . In JMP do: 1 2 We wish to estimate the mean/average revenue of the subpopulation of stores that spent x ? = 200 on advertising. Suppose that we also wish to predict the revenue in a future month when our store spends x ? = 200 on advertising. The point estimate in both situations is the same: y = ? 100 + 7 ? 200 ? 1300 the corresponding standard errors of the mean and of the prediction however are di? erent: SE µ ? 331. 663 SEy ? 690. 411 40 / 47 Choose Fit Model From response icon, choose Save Columns and then choose Predicted Values, Std Error of Predicted, and Std Error of Individual. Stat 326 (Spring 2013) Introduction to Business Statistics II 39 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II Inference about Prediction Estimation and prediction (cont’d) Note that in the appliance store example, SEy SE µ (690. 411 versus 331. 63). This is true always: we can estimate a mean value for y for a given x ? much more precisely than we can predict the value of a single y for x = x ?. In estimating a mean  µy for x = x ? , the only uncertainty arises because we do not know the true regression line. In predicting a single y for x = x ? , we have two uncertainties: the true regression line plus the expected variability of y -values around the true line. Inference about Prediction Estimation and prediction (cont’d) It always holds that SE µ SEy Therefore a prediction interval for a single future observation y will always be wider than a con? ence interval for the mean response  µy as there is simply more uncertainty in predicting a single value. Stat 326 (Spring 2013) Introduction to Business Statistics II 41 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 42 / 47 Inference about Prediction Example cont’d: JMP also calculates con? dence intervals for the mean response  µy as well as prediction intervals for single future observations y. (For instructions follow the handout on JMP commands related to regression CIs and PIs. ) Inference about Prediction Example cont’d: To construct both a con? ence and/or prediction interval, we need to obtain SE µ and SEy in JMP for the value x ? that we are interested in: Month Ad. Expend. S ales Rev. Pred. Sales Rev. StdErr Pred Sales Revenues StdErr Indiv Sales Revenues Let’s construct one 95% CI and PI by hand and see if we can come up with the same results as JMP: In the second month the appliance store spent x = $200 on advertising and observed $1000 in sales revenue, so x = 200 and y = 1000 Using the estimated LS regression line, we predict: y = ? 100 + 7 ? 200 = 1300 Stat 326 (Spring 2013) Introduction to Business Statistics II 43 / 47 Need to ? nd t ? ?rst: Stat 326 (Spring 2013) Introduction to Business Statistics II 44 / 47 Inference about Prediction A 95% CI for the mean response  µy , when x ? = 200: Inference about Prediction A 95% PI for a single future observation of y , when x ? = 200: Stat 326 (Spring 2013) Introduction to Business Statistics II 45 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 46 / 47 Inference about Prediction Example cont’d: Advertising exp. Sales Rev. Lower 95% Mean Upper 95% Mean Sales Rev. Sales Rev. Lower 95% Indiv Sales Rev. Upper 95% Indiv Sales Rev. Month Stat 326 (Spring 2013) Introduction to Business Statistics II 47 / 47 How to cite Simple Linear Regression, Papers

Information Technology Project Management Project Renovation

Question: Discuss about the Information Technology Project Management Project Renovation. Answer: Project Definition In an overview, it is an Expansion project over Dental R Us. The project includes renovation on the existing system with replacement of another new system. The assignment emphasizes on the incorporation of interactive system on word processing, reports making over Dental care in the selected organization. The project shows the background of the case study with including the essential details of the agenda (Marchewka 2014). The project includes objectives for showing related initiatives for implementation. Then the constraints and assumptions are included for charter making. The scope and exclusions are shown to determine the project area of study. The outcomes are included for showing the parameters of the activities. The project undergoes with a business case model according to PRINCE2 template for standard consideration of project management. Background The Dentists R Us Company is currently undergoing an expansion that has resulted in significant growth and development in the practice. For this reason, the Practice Manager has suggested the owner of Dentists R Us to consider investing on incorporating a Dental Practice Management System. The project deals with the implementation of this information system that will facilitate an efficient and automated means to carry out the day to day operations such as managing records, appointments, patient information, accounts, billing and payment details, reminders and so on (Olson 2014). Thus, the project aims to transform the existing paper based approach and facilitate a new information system for management operations in Dentists R Us. Project Objectives The basic objectives of the particular project Dental Practice Management System (DPMS) are described underneath: 1. To develop a computerized information system that automatically performs to daily operations such as billing, managing patient records, scheduling reminders and appointments, processing payments (Kerzner 2013) 2. To transform the existing paper based approach to computerized management system in an attempt to reduce time and efforts for manual activities as well as increase efficiency with a time and cost saving solution Desired Outcomes The desired outcomes for the particular project Dental Practice Management System are as follows: 1. The companys personalized software that provides a digital platform for computerizing daily operations (entering information, storing records and retrieving required details, processing payments, automated appointment scheduling) 2. Access to a fully functional backend company database for storing individual details about patients/ customers, employees, appointment schedules as well as payment histories and records The system is divided into two primary components: Subsystem 1 Handles processing of patient records including appointments, reminders Subsystem 2 Performs billing, accounts and payment processing Project Scope and Exclusions The background of the study is based on Dental R Us, a case study on traditional dental care for the distant patients and in-house patients as well. The primary infrastructure of Dental R Us is entirely dependent on legacy paperwork based work process. In that manner, the dental care organization faces several difficulties in record maintenance, information handling, and proper response to the emergency patients (Gido and Clements 2014). The new system will be implemented for maintaining better operation facility with including new architecture of system implementation. The project will help to include information technology over the provided scenario to mitigate the issues and difficulties. The improvement lies on the new system implementation over the given scenario of Dental care organization. Exclusions The following are not included in the current scope of this project: 1. Web based/ online customer interaction platform 2. Online payment gateway to facilitate electronic payment 3. Email/ message alert mechanisms Constraints and Assumptions The constraints of this particular project are outlined below: 1. A predetermined budget 2. Limited time/ short duration for implementation 3. Limitation in human resource/ staff and their corresponding skills Assumptions 1. The company employees/ users of the newly proposed information system need to be appropriately and adequately trained before going live with operating the software application 2. A detail documentation i.e. user manual needs to be developed for future reference as well as handling and managing the system in an effective manner 3. A prototype of the actual information system needs to be released for testing phase in order to develop test cases for analysis and evaluation before going live with the final version of the system (Lance, Luper and Haigh 2013) User(s) and any other known interested parties The classifications of the types of users of the newly developed information system Dental Practice Management System (DPMS) are categorized as follows: Organizational staff/ employees: This category of users is allowed to access portions of database. They can insert new information for adding and storing records retrieve certain details about patients, their payments and appointments and process requests/ orders and financial transactions, billings System administrators: They are responsible for managing and supervising the information system (Usui 2012). They are privileged to modify and delete all records, monitor and track any individual records as well as supervise activities of any other users of the information system Interfaces For developing the information system, developers utilize Application Programming Interfaces (APIs) (Schwalbe 2015). The API incorporates specific programming/ coding languages and designing tools. The tables below demonstrate the interface specifications. Platform Tools Role MS Windows 7 Apache Tomcat Application Server Eclipse IDE HTML, JavaScript, CSS, JSP Coding My SQL Server Stored Procedure, trigger, constraints, queries (DDL, DML) Database Hardware Interfaces Hard Disk Drive 320 GB Processor Dual Core Processor Memory 2 GB Figure 1: Interfaces and technology levels for DPMS (Source: Raymond and Bergeron 2015, pp.56) Project Approach The information system development approach follows a RAD (Rapid Application Development) model. The key phases in this approach are: Business modeling The business case is designed along with a detail identification of factors driving project success. Data modeling Data sets, attributes and associations are identified and defined. The relationships among the data models, data flow and interlink are defined and established (Too and Weaver 2014). Process modeling Data modeling are converted into business model using CASE (Computer Aided Software Engineering) tools. Application generation The application is developed using object oriented techniques, code generators, programming languages and DBMS (Database Management Systems). Testing and turnover The overall testing time is comparatively small as it follows an iterative framework and prototypes are tested independently after each of the iterations is performed (Mitti et al. 2014). Business Case Reasons The paper based approach is not sufficient for expansion of the dental practice business of Dentists R Us. The DPMS information system will effectively manage the growth and development of the company by increasing efficiency and performance through automation. Business options The project will help to include information technology over the provided scenario to mitigate the issues and difficulties with existing management techniques. Expected benefits 1. Reduced costs of operations 2. Saving time and effort on manual tasks 3. Less time required for processing and handling day to day activities Expected Disadvantages Possible challenges include: 1. Organizational change 2. Business process change 3. Staff may find it difficult to become accustomed with the new system Timescale The time constraints are significant as, due to the rapid growth there is limited duration for accomplishing the outcomes and deliverables (Turner 2014). The project is expected to start by June 2016 and the final version is to be released by December 2017. Costs The estimated cost for the project is 1400.56 US dollar. Major risks Few of the major risks involve: 1. Lack of organizational policies 2. Incompatibility with organizational culture 3. Lack of skills to operate new system References Gido, J. and Clements, J., 2014.Successful project management. Nelson Education. Kerzner, H.R., 2013.Project management: a systems approach to planning, scheduling, and controlling. John Wiley Sons. Lance, A., Luper, D. and Haigh, N., Motive Power, Inc., 2013.Project Management System and Method. U.S. Patent Application 13/935,343. Marchewka, J.T., 2014.Information technology project management. John Wiley Sons. Mitti, A.R., Paget, M., Zambrano, C. and Hampton, T., General Electric Company, 2014.Project management system and method. U.S. Patent Application 14/220,198. Olson, D., 2014.Information systems project management. Business Expert Press. Raymond, L. and Bergeron, F., 2015. Impact of Project Management Information Systems on Project Performance. InHandbook on Project Management and Scheduling Vol. 2(pp. 1339-1354). Springer International Publishing. Schwalbe, K., 2015.Information technology project management. Cengage Learning. Too, E.G. and Weaver, P., 2014. The management of project management: A conceptual framework for project governance.International Journal of Project Management,32(8), pp.1382-1394. Turner, J.R. ed., 2014.Gower handbook of project management. Gower Publishing, Ltd.. Usui, H., 2012.Project management system. U.S. Patent 8,290,805.